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Title: A Digital Twin Framework for Mechanical Testing Powered By Machine Learning
Authors: Kahya, M.
Söyleyici, C.
Bakır, M.
Ünver, H.Ö.
Keywords: Digital twin
fatigue estimation
machine learning transfer learning
Fatigue of materials
Machine learning
Mechanical testing
Aviation industry
Fatigue estimation
Learning Transfer
Machine learning transfer learning
Materials and process
Minimum weight
Strength to weight ratio
Transfer learning
Issue Date: 2022
Publisher: American Society of Mechanical Engineers (ASME)
Abstract: The aviation industry demands innovation in new materials and processes which can demonstrate high performance with minimum weight. Strength-to-weight ratio (STR) is the key metric that drives the value justification in this demand stream. However, aviation's test and certification procedures are time-consuming, expensive, and heavily regulated. This study proposes a Digital Twin (DT) framework to address the time and high costs of mechanical testing procedures in the aviation industry. The proposed DT utilizes new Machine Learning (ML) techniques such as Transfer Learning (TL). Hence, a proof-of-concept study using TL in the Aluminum material group has been demonstrated. The promising results revealed that it was possible to reduce the test load of new material to 40% without any significant error. Copyright © 2022 by ASME.
Description: ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022 -- 30 October 2022 through 3 November 2022 -- 186577
ISBN: 9780791886656
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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